potential future climatic conditions on tourists: a case

Galdies, C. (2015).Xjenza Online, 3:86–104.

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Research Article

Potential future climatic conditions on tourists: A case study focusingon Malta and Venice

C. Galdies∗11Division of Environmental Management and Planning, Institute of Earth Systems, University of Malta, Msida,Malta

Abstract. The main purpose of this study is toquantify important climatic shifts that took place overMalta and Venice that could be considered as a determ-ining factor on their choice as two prime tourist destin-ations. Rather than making use of traditional touristclimate indices, this study identifies long-term trendsin weather variables and their derived bioclimatic in-dices. These climate derivatives are based on a setof high temporal observations (some of which are col-lected every 30 minutes) and are thus able to capturevaluable information that traditional monthly distribu-tion cannot provide. The derivatives obtained from theelementary meteorological observations showed that thelevel of comfort experienced by visiting tourists over thelong term is deteriorating due to increased heat stress.Nonetheless, the increased occurrence of optimal windspeed conditions, as well as a reduced occurrence of galestorms and wind chill events is making these destina-tions more attractive. A careful study of the output ofIPCC climate model projections sheds light on a criticalfuture bioclimate condition during current peak visitingmonths (July and August) at both destinations. Thismay imply a required shift, as a form of adaptation,of the visiting periods at these two destinations. Thisstudy should allow tourist planners to determine whichweather element is a likely future obstacle to the overallbioclimatic suitability of outdoor tourism activities.

Keywords: Climate change, climate trends, thermalbioclimate indices, Climate projections, adaptationmeasures, Mediterranean

1 Introduction

The opportunity for tourists to enjoy themselves andrelax by participating in outdoor activities that differ

from their daily routine and surroundings, defines theselection of particular destinations (Dann, 1981). Theseactivities are affected by their intrinsic climatic nature(Becken & Wilson, 2013) or other temporal factors suchas seasonality (Gartner, 1986). In the tourism sector, a‘favourable climate’ can be regarded as a resource, anddestinations with climate resources of a better qualitythan others always enjoy a competitive advantage. In-deed, optimal variations in temperature, humidity orsnow depth (as in the case of high altitude places) favouroutdoor activities at specific times of the year.

Economically, a strong link has developed betweenthese destination-based activities and the employmentgenerated, which however could be disrupted in the faceof a changing climate (Olsen, 2009). Thus there is apertinent need to assess and evaluate the suitability ofthe destinations’ climate for tourism, in order to aiddecision-making by tourists as well as the tourism in-dustry itself in better assessing the state of the destin-ation for tourism development, identify any significanttrends, and incorporate adaptation strategies in tourisminfrastructure planning and programming.

Many investigators looked at the possibility of us-ing an index-based approach that considers the multi-faceted nature of weather and the complex ways in whichthe weather variables come together to give meaningto local climatology for tourism (Mieczkowski, 1985; deFreitas, Scott & McBoyle, 2008). The formulation ofsuch climate indices have evolved from the more gen-eral development of climate indices in sectors such ashealth (e.g. Wind Chill, Humidex, etc.) and agriculture(e.g. various drought indices). A key issue in their con-struction is the selection of the right weather variables.Smith (1993), and Matzarakis and Moya (2002) sug-gested that the weather parameters affecting tourists’comfort and safety should include air temperature, hu-

*Correspondence to: C. Galdies ([email protected])

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87 Potential future climatic conditions on tourists: A case study focusing on Malta and Venice

midity, radiation intensity, wind, cloud cover, sunshineduration and precipitation. de Freitas (2003) classifiedclimate according to its thermal, physical and aestheticaspects, where the thermal aspect incorporates air tem-perature, humidity, wind and solar radiation; the phys-ical aspect includes rain and wind; and the aestheticaspect relates to sunshine or cloud conditions.

Tourist-related climatic indices range from single fa-cet indices (e.g. Becker, 2000) to other more elabor-ated ones (e.g. Mieczkowski, 1985; Morgan et al., 2000).One of the most comprehensive schemes proposed so faris that by Mieczkowski (1985) who developed a Tour-ist Climate Index (TCI) that merges seven features ofclimate into a single climate index for general tour-ism activities. However, the application of such indicesmust be carried out with caution since the level of vis-itors’ perception to these variables, coupled to any be-havioural adaptation makes their preference and exper-ience even more complicated (de Freitas et al., 2008).Moreover, such indices have not necessarily been valid-ated against tourists’ preferences or visitation data, andthus lack any objective rating and weighting schemesused for individual climate variables. Other weaknessesstem from their failure to address the essential require-ments of a comprehensive index as highlighted by thestudy conducted by de Freitas et al. (2008).

Kovacs and Unger (2014) have recently slightly mod-ified Mieczkowski’s TCI by including a more realisticindex that is related to thermal comfort conditions, inorder to look at the suitability of Hungarian climate forgeneral tourism purposes (such as sightseeing, shopping,and other light outdoor activities). They also managedto adjust the TCI to a ten-day scale rather than theoriginal monthly averages of the climatic parameters,making it more relevant to tourism. This shows thatresearch on tourist climate indices is ongoing so as tomake them more relevant to the tourism sector.

This study does not make use of the originalMieczkowski’s index or any of its most recent deriv-atives, which is beyond the scope of this article, butrather identifies long-term trends in weather variablesand makes use of published bioclimatic indices thatcould point to the prevalence of less favourable weatherconditions at selected tourist destinations, and whichcould potentially favour a shift in the visitation pat-terns. The method presented here analysed a wide rangeof weather variables collected from two tourist destina-tions at high temporal resolution, thereby addressingthe weather conditions that tourists actually experienceduring their stay in these locations.

The main purpose of this study was to identify anylocalised current and future trends of weather variablesas a quantitative approach for measuring the state ofsuch a tourism resource. Its scope was to expose any

statistically significant, long-term changes in weathertrends as potential outcomes due to a changing climate,and to see if they can be termed as critical to the tour-istic experience. This study also looked at how theseweather parameters and related bioclimatic indices areexpected to deteriorate at both destinations by 2070.

Important studies have shown that climate changecan substantially redistribute climate resources acrossregions and between seasons, with particular referenceto well-established and emerging Mediterranean destin-ations (e.g. Lanquar, 2011). In this respect, this studyis a step forward towards the needed quantification ofthe statements published by IPCC (2014) that Medi-terranean tourism is likely to be impacted by climatechange.

This paper examined whether two popular destina-tions – Malta and Venice – are and will be experiencingany shifts in those climatic factors considered to beimportant factors influencing the travelling choice.The selection of these two locations is based on bothpractical and geographical reasons, namely: (1) theavailability of consistent and long-term weather ob-servations at high temporal resolution and (2) thedifferent climatologies owing to the difference in latit-udinal positions of the two locations, thus offering twoindependent scenarios for the study.

Destination 1: Malta

In Malta tourism is one of the main economic pillars.In 2010, British, Italian, German, French and Span-ish tourists accounted for 68% of all visitors (EuropeanCommission, 2014). The success of the tourism sector,which generates 8.3% of the total Maltese employment(compared to the 1% in other EU countries; EURO-STAT, 2008), originates mainly from its Mediterraneanclimate (Galdies, 2011), which provides an ideal year-round destination.

According to the Malta Tourism Authority, in 2013,a total of 1,582,153 inbound tourists visited Malta withan average length of stay of 8 days. During that year,the total tourist expenditure per capita amounted to910 Euros. The three most popular tourist months wereJuly, August and September. The three main motiva-tions for choosing Malta in 2013 was the agreeable cli-mate (57%), new destination (46.7%) and history andculture (39.2%). During that year swimming, walkingand hiking constituted 95% in terms of tourist particip-ation in sport and outdoor activities. Sightseeing wasthe highest activity registered in 2013, followed by visitsto historical sites and churches.

A total of 97.2% of tourists travelled to Malta byair, while the remaining tourists arrived by sea. Thetotal number of cruise passengers in 2013 was 431,397.

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Potential future climatic conditions on tourists: A case study focusing on Malta and Venice 88

Future climate change policies to mitigate the emissionsof greenhouse gases however may result in problems ofconnectivity to Malta, both by air as well as by sea(European Commission, 2013; Roson & Sartori, 2014).

Destination 2: Venice Lagoon, Italy

As to Venice Lagoon, art and culture are the maintouristic attractions and many tourists visit Venice la-goon because of its unique location as well as its culturalhistory. It has a major port, which is the most import-ant in the Mediterranean for cruise tourism and ranks10th worldwide in terms of the number of passengersit receives. In 2011, the total arrivals reached almost5 million. During that year, the US accounted for themost inbound tourists to such a destination, whereasthe traditional European markets were constituted ofFrench, British and German tourists (Euromonitor In-ternational, 2012).

During the month of August 2013, international vis-itors to Venice lagoon amounted to 1,515,956 (SezioneSistema Statistico Regionale on Istat: Regione Ven-eto, 2014), while domestic tourist arrival amounted to8,240,596 during the same year. The least popularmonth is January, with a total of 201,394 of total ar-rivals.

Tourism will continue to be one of the most import-ant economic activities at both locations, and to remainsustained by market-driven forces. Their tourist typeis composed of a ‘residential’ and ‘day-tripper/cruise-tourists’ type, where the latter category is also becomingpopular in Malta in view of its developing cruise-linertourism.

2 Methodology

2.1 Identification of Current Climatic Trends

Rather than applying climate indices for tourism, thisstudy considers (1) the identification of climatic trendsof the main weather parameters, such as air temper-ature (maximum and minimum temperature; air pres-sure; occurrence of heat waves) that are good indicat-ors of a changing climate within the Mediterranean re-gion, and (2) essential characteristics that defines anoptimal outdoor recreation environmental setting. Thisapproach seeks to integrate the factors that influence thebody-atmosphere thermal state by considering ambientair temperature, relative humidity, vapour pressure andwind speed. It also provides perceptive indices producedand used by international weather services (such asNWS/NOAA and Environment Canada, where both in-dices are applied by Malta Meteorological Office duringthe summer period (http://Maltairport.com/weather)and by ARPA (Franco & Gabriele, 2001)). These werechosen because of their relation to the net thermal con-

tent of the human body. Variables studied included rainand wind speed. Aesthetic facets such as sunshine orcloud cover were not included here due to the overallmild Mediterranean weather.

In addition to elementary weather parameters suchas precipitation, humidity, wind speed and temperaturemaxima, minima and anomalies from the climate normof the two destinations (based on the 1961–1990 climatenormal), the present study adopts a three-level climaticapproach similar to the one published by Yu, Schwartzand Walsh (2009) using a mix of elementary and biocli-matic approaches. In their study, they (1) combinedhigh temporal weather observations that are more rel-evant to tourism, (2) addressed the overriding nature ofindividual weather elements (instead of arbitrarily as-signing weights, as in the case of the Tourist ClimateIndex), and (3) integrated multiple weather elementsthat affect the quality and suitability of weather condi-tions for outdoor tourism (Matzarakis & Moya, 2002).Regarding point (1) above, it is important to note thatfrequent weather observations contain more valuable in-formation than statistical daily or monthly data sinceweather elements with high temporal variation such asrain, thunderstorms and visibility are closely related tothe viability of outdoor tourism activities and so can-not be ignored. The use of sub-hourly weather observa-tions provides a greater chance of capturing the weathervariability that could be of specific relevance to differentoutdoor tourism activities. Moreover, based on a surveyto gauge tourist perception, Hamilton and Lau (2005)found that there are more than one significant tourism-related climate attributions in addition to ambient airtemperature. de Freitas (2004) showed that, within abroad range of ‘non-extreme’ thermal conditions, sev-eral different factors are important in determining thepleasantness rating of given climate conditions. For ex-ample, the non-thermal elements of rain, high wind andlow visibility were shown to have considerable impactson tourists’ satisfaction.

The three-level climatic approach used in this studywas determined by the availability of weather variablesin historical weather archives, which are based onthe following three weather components: perceivedtemperature, wind and significant weather (the latterultimately defining the overall visibility).

• Perceived temperature combines temperature, re-lative humidity and wind. It is represented bythe Wind Chill Index in the winter (the combinedphysiological effect of air temperature and windspeed; National Weather Service, 2001) and by theHumidex during summer (Masterton & Richardson,1979). Low or high perceived temperatures are un-comfortable and can be harmful to tourists.


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Figure 1: Sub-indices starting from observed, high temporal elementary weather observations.

• Strong wind can considerably degrade tourist sat-isfaction and affect outdoor safety.

• Significant weather encompasses different weatherconditions such as rain, hail, lightning, fog, visibil-ity, smoke and dust storms.

A sub-index was created for each of these weathercomponents representing perceived temperature interms of Wind Chill and Humidex, Wind, and Signi-ficant Weather (Fig. 4. Each sub-index was scaled tothree levels of suitability for outdoor tourism activit-ies (1 = unsuitable conditions, 2 = critical conditions,and 3 = optimal conditions) indicated by upper andlower thresholds. The thresholds and sub-index categor-ies are summarized in the Appendix. The criteria usedto scale the sub-indices are flexible and can be modi-fied to best fit the characteristics and requirements ofspecific activities, and their thresholds can be easily de-termined by tourist experts or from surveys of tourists.The thresholds can be modified to fit any activities forwhich reasonable weather-related thresholds for touristinvolvement can be made available.

2.2 Thermal Bioclimate Indices

In support of the above approach, the analysis ofthermal bioclimate is also of high interest to the healthand recreation tourism sectors. The estimation of mod-

ern bioclimatic indices, based on human energy balance,presents an adequate method for the quantification ofthe human thermal bioclimate under different meteoro-logical conditions (Matzarakis, 2006; Matzarakis, Rutz& Mayer, 2010).

This study thus analysed three thermal stress indicesthat highlight potential hazardous conditions to visitors.These include the following:

1. the Thermal Sensation (TSN) equation (Givoni& Noguchi, 2004);

2. Becker (2000) Human Bioclimatic Comfort(HBC), and

3. the shade Apparent Temperature (ATshade)equation (Morabito et al., 2014).

(1) the Thermal Sensation (TSN) equation can be cal-culated as follows,

TSN = 1.2 + 0.1115Ta + 0.0019S–0.3185V, (1)

where TSN is the thermal sensation scale ranging from1 (very cold) to 7 (very hot), while 4 is neutral, Ta is theair temperature (◦C), S is the solar radiation (Wm−2),and V represents the wind speed (m s−1).

Thus for an average person under an environmentalcondition with an air temperature of 28 ◦C, solar radi-ation of 150 Wm−2, a wind speed of 1.9 m s−1 over the

Table 1: Monthly average solar radiation over Malta and Venice in Wm−2.

S(Wm−2)

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Qrendi(Malta)

116.67 145.83 208.33 242.50 305.42 322.92 322.92 295.42 232.08 172.92 132.50 95.83

VeniceLagoon

48.61 81.57 130.55 185.42 235.65 259.26 267.01 228.24 170.02 109.03 56.25 40.86


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Table 2: Degree of cooling power of the Human Bioclimatic Comfort thresholds (Farajzadeh & Matzarakis, 2012).

HBC (µ cal cm−2s−1) Air situation Type of bioclimatic stimulation

0 to 4 Hot-sultry – uncomfortable Bioclimatic pressure5 to 9 Warm-comfortable Bioclimatic comfort10 to 19 Mild-pleasant Bioclimatic comfort20 to 29 Cool Moderate stimulation30 to 39 Cold – slightly uncomfortable Middle to intense stimulation40 to 49 Moderate – very uncomfortable Intense stimulation50 to 59 Unpleasant – extremely cold Intensive pressure

body would be needed in order for the person to remainin neutral, thermal sensation condition (Cheng & Ng,2006).

For this study, the solar radiation over Maltawas based on monthly values published by Farrugia,Fsadni, Yousif and Mallia (2005) and converted fromkW h m−2 day−1 to Wm−2. Values related to the Venicelagoon were derived from WMO standard normal for theperiod 1961–1990 and converted to Wm−2 (Table 1).

(2) the Human Bioclimatic comfort can be calculatedas follows:

HBC =(0.26 + 0.34V 0.632

)(36.5 − Ta), (2)

where, HBC is the human bioclimatic comfort in termsof cooling power of the environment at (µ cal cm−2s−1),V is 10 m wind speed (m s−1), and Ta is the air temper-ature (◦C).

According to Becker (2000), when the HBC value isless than 5 or exceeds 20, then a bioclimatic pressureon the human body occurs. Table 2 shows the degreeof cooling power of the Human Bioclimatic Comfortthresholds.

(3) the Apparent Shade Temperature (ATshade) canbe calculated by taking into account the air temperatureand humidity,

ATshade = Ta + 0.33e–0.70V –4.00, (3)

where, Ta is the air temperature (◦C), V is 10 m windspeed (m s−1), and e represents the water vapor pressure(hPa).

2.3 Future Climate Projections Using theLatest CMIP5 Climate Data (IPCC, 2014)for the two Destinations.

In this study, CMIP5 future climate projections presen-ted by IPCC (2014) 5th assessment report were used.Specifically, the modelled projections produced by theMet Office Hadley Centre HadGEM2-ES coupled EarthSystem Model (Collins et al., 2011) were assessed. Theclimate model comprises of an atmospheric GCM andan ocean GCM, the latter is deemed important for ap-propriate simulation of the future climate in parts of

the Mediterranean basin. Additional model componentsinclude terrestrial and ocean carbon cycle and tropo-spheric chemistry. According to Martin et al. (2011), theearth system components of HadGEM2-ES comparedwell with observations and with other models, and wasconsidered to be a valuable tool for predicting future cli-mate and for the understanding of the climate feedbackswithin the earth system. HadGEM2-ES has been usedto perform Hadley Centre’s contribution to the CMIP5modelling activity for IPCC’s 5th Assessment Report.

Global gridded monthly values of the 2-m minimumand maximum temperature and precipitation were ana-lysed (Fig. 2). These datasets were downscaled to re-flect local impacts by means of established techniquesin order for managers to interpret global climate pro-jection information at the local scale. Detailed qualityassurance information of downscaled CMIP5 have beenpublished (Brekke, Thrasher, Maurer & Pruitt, 2013).The monthly values were extracted over the two areas ofinterest for the four pathways. The Human BioclimaticComfort was calculated on the basis of these projectedvalues taking into account the detected trends in windspeed over the two geographical locations.

2.4 Data analysis

Weather observations from two WMO climate stations,i.e. Malta (WMO 16597 Latitude: 35:51N Longitude:014:29E) and Venice (WMO 161050 Latitude: 45:30NLongitude: 012:20E) were collected for this study.

From these observations, a normalised yearly analysiswas carried on the data measured during the observationperiods. The correlation between the elementary annualobservations (such as wind speed, air temperature, hu-midity, etc.) and the frequency of a particular condition(i.e. unsuitable, critical and optimal conditions) at eachof these destinations provides a measure of the poten-tial sensitivity of tourism-related climate attributes tochanges in that weather conditions (such as changes thatare expected to be driven by global warming).

Temperature anomalies were derived to provide an ac-curate description of climatic variability and allow datacomparison from different climatological areas. WMOrecommends 30 years as a standard period for the ana-


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Figure 2: Subset example of the HadGEM-ES RCP 8.5 climate model projection showing distribution of maximum temperature forJuly 2070.

lysis of climate anomalies (Folland, Karl & Vinnikov,1990), and the climate period between 1961 and 1990compiled for the two destinations is used as a typicalbaseline in conformity with the IPCC and other officialclimate centres.

In addition to ambient air temperature, dew pointtemperature, wind speed and atmospheric pressure, thefollowing climatic parameters were calculated as shownin Table 3.

3 Results

3.1 Climatic Indices Over Time

Weather observations from the two destinations, Luqa -Malta (1967–2013 except 1971, 1972 and 1973; averagecompleteness throughout: 82%) and Venice - Italy(1973–2013; average completeness throughout: 84%)were used to illustrate how the elementary weatherparameters as well as sub-indices could be applied tomeasure how climate, as a resource for tourism, haschanged with time.


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Table 3: Computation of the various climatic indicators based on published equations.

Climatic parameter Reference

Heat Index Masterton and Richardson (1979)Wind Chill Index (◦C) National Weather Service (2001)Presence of Heatwave WMO; UK Met Office (2014b)Relative Humidity (%) Alduchov and Eskridge (1996)Windy conditions Yu, Schwartz and Walsh (2009)Gale storms The Beaufort scale (UK Met Office (2014a))Adverse weather World Meteorological Organization (2014)Precipitation (specific) World Meteorological Organization (2014)Visibility World Meteorological Organization (2014)Thermal Sensation Index Givoni and Noguchi (2004)Human Bioclimatic Comfort Becker (2000), Farajzadeh and Matzarakis (2012)Apparent Shade Temperature Morabito et al. (2014)

(i) Trends over time: elementary weather parameters(annual minimum and maximum temperature,heat-wave events, and anomalies from the climatenorm).

Results show localised warming trends of the annualhighest maximum temperature at both destinations,with +0.09 ◦C of +0.63 ◦C (per annum) in Malta andVenice respectively (Fig. 3). The Mann–Kendall testshows that the positive trends observed in both casesare significant at the 99% confidence level.

Results also show similar warming trends in the an-nual lowest minimum temperatures of +0.02 ◦C and+0.04 ◦C per annum in Malta and Venice respectively(Fig. 4). The Mann–Kendall test shows that the posit-ive trend observed over Malta is significant at the 99%confidence level. This shows that throughout the yearsthe incidence of warmer nights has become increasinglycommon with time.

Figures 5(a) and 5(b) show the annual mean airtemperature anomalies registered in Malta and Venicecompared to the climate norm of 1961–1990. In bothcases, the Mann–Kendall test confirmed that the posit-ive trends observed are significant at the 95% confidencelevel. Venice seems to have been impacted by a posit-ive temperature anomaly since 1985, unlike Malta. In aprevious study (Galdies, 2012) showed that the rate oftemperature increase over Malta for a longer period of1951–2010 was 0.2 ◦C per decade and already falls mid-way within the range of 0.1 ◦C and 0.4 ◦C as describedby Hertig and Jacobeit (2008) for the predicted annualwarming for the period 2020–2030 over southern Europeusing IPCC-approved models.

The occurrence of conditions throughout the entireobservation period, linked to the onset of heat-waveevents at the two destinations, is shown in Figure 6.Results show that summer heat-waves are a more com-mon feature in Malta than in Venice, implying a higher

impact on utility infrastructure and tourist activities,among others, especially in Malta. In spite of the lowerfrequency of occurrence observed in Venice during thestudy period, both trends are statistically significantat 95% confidence level. The severity and frequencyof heat wave events are a representation of the largerregional climate patterns in the Mediterranean and maybe related to the overall changing climatic conditionsat both sites, to the detriment of visiting tourists.

(ii) Climate suitability over time: 3-level sub-indices(wind speed, wind chill index, heat stress and Galestorms).

In the following sections, the results for the occur-rence of “suitable” days derived according to Table 3are presented. These are based on the consolidationof three- and half-hourly weather reports. The an-nual frequency or number of occurrences within eachof the three categories (Unsuitable – Critical – Optimalweather conditions) varied over time and across the twosites.

Results obtained from Malta (Fig. 7(a), 7(b), 7(c))provided some strong evidence regarding the generaltrends. Less windy conditions are increasingly becom-ing more common (Fig. 7(a)), and this is shown by thestatistically positive and significant increasing trend atthe 95% confidence level. During the same long periodof time, both critical and unsuitable conditions (thatis windier conditions) show a negative statistically sig-nificant trend. This a classical example demonstratingthat the optimal weather (in terms of wind speed) is in-creasing at the expense of the other two conditions. Theabove relationship also holds for the occurrence of Galestorms (Fig. 7(c)). All trends are statistical significantat the 95% confidence limit, and only the trend shownby the optimal conditions is positive.

With regards to the frequency of occurrences of wind


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(a) (b)

Figure 3: Annual highest maximum temperature trends over (a) Malta (1967-2013) and (b) Venice (1973-2013).

(a) (b)

Figure 4: Annual lowest minimum temperature trends over Malta (a) (1967–2013) and (b) Venice (1973–2013).

(a) (b)

Figure 5: (a) Malta and (b) Venice annual mean air temperature anomalies for the period 1967–2013 and 1973–2013 respectively.Two-year running average shown in blue. Anomaly is based on Malta’s and Venice’s 30-year climatology of the annual mean airtemperature observed during the climate norm 1961–1990.


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(a) (b)

Figure 6: Total number of occurrences (annual frequencies) when the maximum temperature exceeded the climate mean monthlymaximum temperature by an additional 5 ◦C in (a) Malta and (b) Venice for the period 1967–2013 and 1973–2013 respectively. Theclimate norm of Malta and Venice is based on the 1961–1990 period.

(a) (b)

(c) (d)

Figure 7: Annual frequency of the optimal (green), critical (blue) and unsuitable (red) conditions (blue) for (a) wind speed, (b) windchill, (c) gales and (d) heat stress, based on the computation of elementary observations taken from Malta during the period 1967–2013.

chill events, only the trend for the optimal conditions(i.e. occurrence of minimal wind chill) is statisticallysignificant and positive (Fig. 7(b)). The negative trendsof the other two conditions proved not to be significantat the 95% confidence level.

Of high concern is the temporal trend of the heatstress index, for which positive trends exist for both the

occurrence of the critical and to a lesser extent that foroptimal conditions (Fig. 7(d)). This trend of increasedcritical conditions was statistically significant at the 95%confidence limit. The trend for the frequency of occur-rence of unsuitable (i.e. worst) heat stress conditions isnot significant, although it is definitely a negative onewhich is especially evident during the latter part of the


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(a) (b)

(c) (d)

Figure 8: Annual frequency of the optimal (green), critical (blue) and unsuitable (red) conditions (blue) for (a) wind speed, (b)wind chill, (c) gales and (d) heat stress, based on the computation of elementary observations taken from Venice during the period1973–2013.

observation period.In the case of Venice, Fig. 8(a), 8(b) and 8(c) showed

that the occurrence of less windy days (falling within therange of the defined optimal conditions) has a strongpositive and statistically significant trend, while theother two trends have very low occurrence and are notstatistically significant. The same pattern is seen forthe frequency of gale storms (Fig. 8(c)), which exhib-ited a positive statistically significant trend for optimalconditions (i.e. less occurrence of gale events).

As to the occurrence of wind chill (Fig. 8(b)) resultsshow that the trends of the three conditions exhibited ajump in the frequency of observations after 1993. Thiscould be due to a change in observation methods or in-strument sensitivity, the details of which are not avail-able. However, what is important here is the relativedifference between the three trends, which seems to bequite constant throughout the observation period. Ana-lysis of only the post-1994 data showed that the trendof the occurrence of optimal conditions is the only stat-istically significant one at the 95% confidence level.

The frequency of three conditions dictated by the heat

stress index (Fig. 8(d)) produced statistically significanttrends for the critical and unsuitable heat stress condi-tions and both of these trends are significantly correl-ated with each other at the 95% confidence level (Per-son’s correlation). However, the trend obtained for theoccurrence of optimal conditions is not statistically sig-nificant at the 95% confidence level indicating that thecritical condition for the heat stress is becoming a realconcern.

Results show that the negative precipitation trendover Malta (Fig. 9(c)) is statistically significant at95% confidence level. Both adverse weather conditions(Fig. 9(a)) and increased incidence of reduced visibility(Fig. 9(e)) both have a negative, but statistically non-significant trend. As to Venice both the trend for thedecreased occurrence of significant events and low vis-ibility are statistically significant at the 95% confidencelimit. However, the trend shown by the precipitationevents over Venice is not statistically significant at the95% confidence limit.


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(a) Occurrence of significant (adverse) weather (Malta) (b) Occurrence of significant (adverse) weather(Venice)

(c) Occurrence of precipitation events (Malta) (d) Occurrence of precipitation events (Venice)

(e) Occurrence of low visibility (Malta) (f) Occurrence of low visibility (Venice)

Figure 9: Annual frequency of (a) adverse weather events, (c) precipitation events and (e) poor visibility for Malta, and (b) adverseweather events, (d) precipitation events and (f) poor visibility for Venice, based on the computation of elementary observations.

3.2 Bioclimatic Indices Assessment

3.2.1 Thermal Sensation Index

Figures 10(a) and 10(b) show an increasing positivetrend of TSI at both locations, with Malta showing a

more pronounced and a statistically significant trend atthe 95% confidence level. This index is based on a con-sideration of the effects of the following on the thermalresistance of the visitor: exercise, the respective effectsof clothing on moisture loss and heat loss, ambient tem-


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peratures, atmospheric water vapour, direct solar radi-ation, the degree of wind penetration into the clothing,and the effect of temperature on the thermal conduct-ivity of the clothing (Steadman, 1979).

3.2.2 Apparent Shade Temperature

Both locations (Fig. 11(a), 11(b)) show an increasingpositive trend in the apparent shade temperature. Onlythe trend observed over Malta was statistically signific-ant at the 95% confidence level. This index considers allenvironmental and bodily conditions that affect humanthermoregulation.

3.2.3 Human Bioclimatic Comfort Index

The use of this index was based on its simplicity and thecombination of air temperature and wind speed. Thetemporal trends of the cooling power in relation to thetwo destinations are shown in Fig. 12(a) and 12(b). Forboth locations, the overall trend is a statistically signi-ficant, negative one indicating a deterioration in human

comfort was towards the warm side of the index spec-trum.

3.3 Future Climate Projections HadGEM-ESClimate Model Output

Figures 13 and 14 present the annual cycle of the max-imum and minimum temperature for each location forthe years 2050 and 2070, compared to their climatebaseline of 1961–1990. For both destinations, a sig-nificant rise in both monthly maximum and minimumtemperatures from the baseline levels is evident, with astronger signal for the RCP8.5 scenario. In the case ofMalta, the peak tourist months are expected to experi-ence an increase of around +1.9 ◦C (2050) and +2.0 ◦C(2070) for July and +2.1 ◦C (2050) and +2.2 ◦C (2070)for August for the least harmful scenario (RCP2.6). Forthe Venice lagoon, the increased temperatures will bemore pronounced (Figs. 14(a) and 14(b)).

Based on the above projections, the future humanbioclimatic comfort index, based on the worst case ra-

(a) (b)

Figure 10: Trend of the thermal sensitivity index for (a) Malta (1967–2013) and (b) Venice (1973–2013).

(a) (b)

Figure 11: Shade apparent temperature index for (a) Malta (1967–2013) and (b) Venice (1973–2013).


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(a) (b)

Figure 12: Bioclimatic comfort index for (a) Malta (statistically significant at 95% confidence level) and (b) Venice.

(a) (b)

Figure 13: Projected (a) maximum and (b) minimum monthly temperature over the Maltese islands in 2050 and 2070 against the1961–1990 Climate Norm baseline.

(a) (b)

Figure 14: Projected (a) maximum and (b) minimum monthly temperature over Venice lagoon in 2050 and 2070 against the 1961–1990Climate Norm baseline.

diative scenario modelled for 2070 (RCP8.5) shows crit-ical situations during the peak visiting months (July andAugust) at both destinations (Fig. 15). This could im-ply a required shift, as a form of adaptation, of the vis-itation periods at these two destinations. The shadedarea shows the worst bioclimatic conditions during the

peak tourist months (hot-sultry, uncomfortable; consid-erable bioclimatic pressure; Table 2).

4 Discussion

The study demonstrated a quantitative analysis of hightemporal weather observations and their derivatives


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Figure 15: Expected discomfort on the basis of the human bioclimatic comfort index for both destination by the year 2070. Theshaded area shows worst conditions (hot-sultry, uncomfortable; considerable bioclimatic pressure; Table 2) at both sites during themonths of July and August.

that can be further tuned to match the specificities ofoutdoor-related tourism activities. The study’s maincontributions to the emerging knowledge on this sub-ject are highly relevant in order to assess the possiblefuture touristic attractiveness of these two destinations.The statistically significant, long-term trends for someof the climatic variables and their derivatives studiedhere can be related to the level of increased comfort orotherwise that tourists have so far experienced at bothdestinations.

In particular, this study verified that a definitive shiftin a number of weather patterns has been detected at thetwo destinations. These included an increased incidenceof heat-wave episodes, increased occurrence of highertemperature maxima and minima, as well as an overallpositive anomaly in the mean temperature at the twodestinations. In an extended historical study, Galdies(2012) showed a higher incidence of heat-wave in recentyears over the Maltese Islands.

The long-term positive anomaly trend of the meanair temperature observed over Malta up until 2014 cor-roborated previous studies that further delved into thevarying rates of change of local weather variables (Gal-dies, 2011, 2012). These shifts were linked to the largerregional impact originating from a warmer climate. Therate of increase of the anomaly in the mean temperatureis +1.1 ◦C while the positive trend observed for the max-imum temperature recorded over the Maltese islandscomes to +1.2 ◦C over the time period studied (46 years)which is equivalent to an increase of around 0.1 ◦C peryear in the yearly maximum temperature. The observeddecrease in precipitation events over Malta (P < 0.05∗)implied an environment that is becoming increasinglydry, which was in line with the IPCC forecasts for this

part of the Mediterranean. Over Venice a similar situ-ation was be discerned, especially with regards to theincreased occurrence of heat-waves, as well as to thesignificant positive trends of the maximum temperatureand for the mean temperature anomaly against the cli-mate norm (equivalent to +1.7 ◦C over 30 years). Thenumber of sub-hourly occurrences of bad visibility andadverse weather (such as thunderstorms, precipitation,etc.) were also seen to decrease over Venice (P < 0.05∗).

The indexed derivatives obtained from the elementarymeteorological observations, such as wind chill and heatindex, showed that the level of comfort experienced byvisiting tourists (and equally by the local community)over the long term has deteriorated at both destina-tion, especially with regards to the observed increasein heat stress. This is being balanced by increased oc-currence of optimal wind speed conditions and lack ofgale storms, as well as reduced occurrence of adversewind chill events. These derivatives were based on aset of high temporal observations and are thus able tocapture valuable information that traditional monthlydistribution cannot provide. By applying this methodo-logical framework one is also able to analyse importantsubsets of the day during which certain outdoor touristactivities become more relevant unlike those applied byother studies (such as the one conducted by Amelungand Nicholls, 2014).

However, the robustness of these derivatives to fore-cast the future visitation patterns to these two destin-ations is not straightforward, since attractiveness ulti-mately depends on a complex set of conditions related toeconomy, good marketing, investment (de Freitas, 2003)as well as tourist perception of a changing climate andrelated experiential behaviour, including their country


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of origin (Morgan et al., 2000). Wall (2007) justifiablyargued that it is extremely difficult to generalize con-cerning the possible implications of climate change fortourism. de Freitas et al. (2008), and Perch-Nielsen,Amelung and Knutti (2010) for example, addressed anumber of deficiencies of the use of tourist climate in-dices by devising a theoretically and empirically soundmethod that integrated the various facets of climate andweather into a single index by means of a novel approachto explore the climate preferences of tourists in a de-tailed manner (Rutty & Scott, 2014). The testing ofsuch indices with travellers is evidently an importantarea of inquiry in order to apply an ‘objective’ measureof quality of the climate at tourism destinations (Scott& McBoyle, 2001) by evaluating the appropriatenessof the sub-index rating systems against stated touristpreferences. Moreover, it is worthwhile mentioning theexcellent work of Goh (2012) who studied how travelmotivation can be analysed using a socio-psychologicalframework in order to explore the impact of climate ontourism demand. Research is currently underway bythe author to study related tourist perception and pref-erences of local climate.

Meanwhile, researchers are still resorting to the ap-plication of the tourist climate index that is a derivativeof a mixture of climate variables like temperature, sun-light, precipitation, humidity and wind to forecast tour-ism flows (Amelung & Nicholls, 2014; Roson & Sartori,2014).

4.1 A Bioclimatic Approach

Such an approach was deemed necessary in order to steeraway from presenting an analysis of weather trends. Thehigh temperature preferences of beach tourists, for ex-ample, reveals that an analysis simply based on weathertrends, without reference to the bioclimatic stress onthe human body, would be problematic. What could betermed as ‘critical’ level index could actually be whatbeach users define as ‘optimal’.

Thus including a series of published indices aimed atunderstanding the degree of physiological comfort with achanging environment provides further correlation withthe weather trends that tourists have been witnessingsince the early sixties/seventies at the two destinations.

These bioclimatic indices have significant implicationsfor human adaptations to the physiological climate ofthe Mediterranean. Since the physiological climate af-fects not only human health and activity, but also phys-ical and mental efficiency, it is important that biocli-matic knowledge are exploited for better technologicaland behavioural adaptations of potentially affected vis-itors. This could be translated into a set of adapta-tion measures (as defined by local policy- and decision-makers) to account for the predicted deterioration ofthe human bioclimatic comfort that is predicted by the

two extreme RCP scenarios as modelled by the CMIP-5IPCC climate modelling.

Some areas in which knowledge of physiologic climatemay prove to be useful include the use of appropriateclothing according to the prevailing physiologic climateand type of outdoor activities. For example, in loc-ations where the apparent temperature exceeds 22 ◦C,vigorous sporting activities may be physiologically dan-gerous. The most recent information concerning tour-ist participation in sport and outdoor activities showsthat 47.3% of total visitors engage in walking and hik-ing activities (Malta Tourism Authority, 2014). The restengage in swimming and diving activities and thereforemay not be affected as much by uncomfortable biocli-matic conditions during such practices. Specific seg-ments of future tourists may thus find the results of thisstudy useful in planning for the best period for theirrecreational activities.

4.2 Future Climate

Results of this study show that the climate is alreadychanging, and the intensity and frequency of extremeweather events, such as heat waves may change in thefuture. Recent extreme weather events caused serioushealth and social problems in Europe, particularly inurban areas (Christidis, Jones & Stott, 2015). Thisstudy looked at the likely future scenario with regards toboth meteorological variables such as ambient temper-ature, and a monthly scenario of the human bioclimaticcomfort at the two destinations (Amelung & Viner,2006). Extreme events will pose additional challengesto the management of health risks to both public healthand tourist sector alike. One hopes that the relevanceof this information in light of current and future touristvisitation is taken on board by the respective authorit-ies in Malta and Italy. The next step would be to focuslocal research on modelling the relation between climateand tourist preference as well as on the sustained com-petitive nature of the two destinations. The results ofthis study highlight the need for a proactive adaptationstrategy by the main stakeholders.

In summary, the positive and negative impacts of achanging climate on visiting tourists at these two local-ities, as identified by this study are of practical import-ance to decision makers at the two tourism destinationsanalysed. In the case of Malta, such information hasnever been compiled.

Adaptation options exist for both destinations, butmost of these options are likely to add costs and offeronly short-term relief. Under the observed scenario ofhigher temperatures, questions exist as to whether ad-aptation is realistic, other than a possible shift in tour-ism peak seasons. These changes are likely to createopportunities at both destinations, including increasedemployment and income. However, non-climate factors


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such as the dates of school holidays during summermight still influence families to keep their current visit-ation preferences.

The ability to analyse the past, current and futureclimate leads to a number of important benefits. Thepresent findings show that there are distinct, signific-ant climatological trends over a long period of observa-tion. The ability to extrapolate possible future scenariosbased on statistically sound climatic trends and futureIPCC climate projections can benefit tourism planningregarding future options of outdoor activities at thesetwo tourist destinations. Similar studies for a largerrange of destinations with diverse climates can be car-ried out to extend the geographical scope of this study.

5 Conclusion

Climate change is a two-fold challenge for the tourismindustry. One part of this challenge involves address-ing the impacts of tourism in contributing to climatechange. What is relevant for this study is the secondpart of this challenge, namely what tourism can andneeds to adapt to climate change. This study providesbasic knowledge that is needed to frame the right typeof adaptation measures by highlighting to tourist plan-ners which are those weather element that are likely topresent a future obstacle to the overall bioclimatic suit-ability for outdoor tourism activities at the two destin-ations.

The main findings of this study are summarised asfollows:

• Climatic shifts in terms of an increased rate inthe mean temperature anomaly, with respect tothe climate norm, over Malta (+1.1 ◦C) and Venice(+1.7 ◦C) are linked to the larger regional andglobal impact originating from a warmer climate.

• The observed decrease in precipitation events overMalta (which is statistically significant at the 95%confidence level) points to an environment that isbecoming increasingly dry. Over Venice, a similarsituation can be discerned, especially with regardsto the increased occurrence of heat-waves.

• The number of sub-hourly occurrences of bad visib-ility and adverse weather (such as thunderstorms,precipitation, etc.) were also seen to decrease overVenice (where such trends are statistically signific-ant at the 95% confidence level).

• The derivatives obtained from the elementary met-eorological observations show that the level of com-fort experienced by visiting tourists over the longterm is deteriorating when it comes to the increasedheat stress. However, the increased occurrence ofoptimal wind speed conditions and lack of galestorms, as well as a reduced occurrence of adverse

wind chill events, are making these destinationsmore attractive.

• All bioclimatic indices show a deterioration in thedegree of comfort, which is particular distinctive(statistically speaking) in Malta.

• IPCC climate model (in this case, by theHADGEM-ES) predictions show a significant in-crease in maximum and minimum temperature overboth locations, with higher increases over Venice la-goon.

• In the case of Malta, the peak tourist months are ex-pected to experience an increase of around +1.9 ◦C(2050) and by +2.0 ◦C (2070) for July and +2.1 ◦C(2050) and +2.2 ◦C (2070) for August for the leastharmful scenario (RCP2.6). For the Venice la-goon, the increased temperatures are predicted tobe more pronounced.

• The future human bioclimatic comfort index, basedon the worst case radiative scenario modelled for2070 (RCP8.5), is expected to reach critical con-ditions during the peak visiting months (July andAugust) at both destinations. This could imply arequired shift, as a form of adaptation, of the visit-ation periods at these two destinations.

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A Appendix

Table A1: Computation of the various climatic indicators based on published equations.

Three-level climatic approach

Climatic parameter Equation & Scoring Reference

Heat Index HEAT INDEX = AIR TEMPERATURE +(5/9)(VAPOUR PRESSURE − 10). If HI isbetween 30 and 39, then conditions are critical; if HI> 39, then conditions are unsuitable; else conditionsare optimal.

Masterton and Richardson(1979)

Wind Chill Index (◦C) WIND CHILL = (((35.74 + (0.6215 ×TEMPERATURE) − (35.75 ×WIND SPEED0.16) +(0.4275 × AIR TEMPERATURE ×WIND SPEED0.16)) − 32) × 5)/9. If WCI isgreater than 10, then critical; if WCI is less than 10,then conditions are unsuitable; else conditions areoptimal.

National Weather Service(2001)

Presence of Heatwave If temperature is greater than the climatic monthlyaverage maximum temperature (climate normalperiod being 1961–1990) by five degrees then flag asunsuitable condition.

WMO; UK Met Office(2014b)

Relative Humidity (%) Relative Humidity% = 100 × {exp[(17.625 ×DEW POINT TEMPERATURE)/(243.04 +DEW POINT TEMPERATURE)]/ exp[(17.625 ×AIR TEMPERATURE)/(243.04 +AIR TEMPERATURE)]}

Alduchov and Eskridge(1996).

Windy conditions If wind speed is between 14 and 25.5 knots, then con-ditions are critical; if wind speed is > 25.5 knots,then conditions are unsuitable. Else conditions areoptimal.

Yu, Schwartz and Walsh(2009)

Gale storms FORCE 8; mean wind speed 37 knots; 19 ms−1; Lim-its of wind speed (knots) 34–40 knots; Probable waveheight is 5.5 meters; Probable maximum wave heightis 7.5 meters. If wind speed is between 22 and 33knots, then moderate conditions; if wind speed is> 33 knots, then strong wind conditions. Else condi-tions are optimal.

The Beaufort scale UK MetOffice (2014a)

Adverse weather Occurrence of SH, TS, FZ, DZ, RA, SN, SG, IC, PL,GR, GS, UP, BR, FG, FU, VA, DU, SA, HZ, PY, PO,SQ, FC, and SS (see Annex 1 of WMO document)

World Meteorological Or-ganization (2014)

Precipitation (specific) Occurrence of SH, DZ, RA, SN, SG, GR, GS (seeAnnex 1 of WMO document)


Visibility Occurrence of FG, FU, VA, DU, SA, HZ, PY, PO,SQ, FC, SS (see Annex 1 of WMO document)



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potential future climatic conditions on tourists: a case

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